Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm

Zhao, Ran; Lee, Dong Hwan; Lee, Hong Kyu

doi:10.5391/ijfis.2015.15.1.12

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…These intensity weight Table IV. Comparison of path length traced by Zhao et al 43 and Aouf et al 44 with path obtained by neural network integrated modified DAYANI approach by considering 1 cm = 1 unit.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, path length traced by some existing authors implementing optimized fuzzy controller using genetic algorithm method and TLBO-based ANFIS technique is considered for comparison purpose. Figure 11(a) and (b) shows the comparison of the path obtained by Zhao et al 43 using an optimized fuzzy controller through genetic algorithm method and our proposed modified Neural DAYANI method. Similarly, the path comparison of the proposed technique with TLBO-based ANFIS technique as obtained by Aouf et al 44 is represented in Figure 12(a) and (b).…”

Section: Comparison With Existing Navigational Controllersmentioning

confidence: 99%

“…11. (a) Path obtained by optimized fuzzy controller by genetic algorithm method by Zhao et al 43 and (b) path obtained by neural network integrated modified DAYANI approach.…”

Section: Technique Used Path Length (Unit) Deviation (%)mentioning

confidence: 99%

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Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Chhotray¹,

Parhi²

2019

Robotica

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SummaryThe present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot’s probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison With Existing Navigational Controllersmentioning

confidence: 99%

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Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Chhotray¹,

Parhi²

2019

Robotica

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“…Use only the applicable degree function, do not ask for other prerequisites or auxiliary information, because this is the goal. Restricted functions and conditions are not so restrictive, nor do they need to be distinguished or continuum [16]. In addition, the search scope covers the space of all independent variables, and it is more likely to seek the best global solution after iteration.…”

Section: Date Mining Statistic Analysis Representationmentioning

confidence: 99%

Real Time Neural Network Path Planning Algorithm for Robot

2021

IJFET

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The emergence of robots not only changed the traditional industrial production mode, but also greatly promoted the progress of social civilization. Whether in daily life or in industrial production practice, the technical level of robots is improving every day, which emphasizes the high level of national science and technology. Robot path planning technology is an important part of robot research. The purpose of this paper is to focus on the research of robot path planning algorithm, learning and in-depth development. This paper introduces the discovery and communication of robot real-time neural network planning algorithm in machine learning, and analyzes and studies it. In order to study the robot real-time neural network path planning algorithm, through the experimental comparison of different algorithms, focus on exploring the effect of different algorithms on path planning. The research results show that the algorithm speed of robot on real-time network path is 24% higher than that of normal network path algorithm, and can be increased to 30% after deep mining, and the efficiency of machine algorithm can be increased to 35% under more complete algorithm. Therefore, the robot real-time neural network path planning algorithm can complete the task more efficiently.

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“…In our previous work [15] we studied the navigation problem for only one robot in the static environment. In this paper, we focus on the proposed approach for multiple robot system in completely unknown dynamic environment based on fuzzy inference system.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Based Navigation for Multiple Mobile Robots in Indoor Environments

Zhao

Lee

2015

Int. J. Fuzzy Log. Intell. Syst.

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The work presented in this paper deals with a navigation problem for multiple mobile robot system in unknown indoor environments. The environment is completely unknown for all the robots and the surrounding information should be detected by the proximity sensors installed on the robots' bodies. In order to guide all the robots to move along collision-free paths and reach the goal positions, a navigation method based on the combination of a set of primary strategies has been developed. The indoor environments usually contain convex and concave obstacles. In this work, a danger judgment strategy in accordance with the sensors' data is used for avoiding small convex obstacles or moving objects which include both dynamic obstacles and other robots. For big convex obstacles or concave ones, a wall following strategy is designed for dealing with these special situations. In this paper, a state memorizing strategy is also proposed for the "infinite repetition" or "dead cycle" situations. Finally, when there is no collision risk, the robots will be guided towards the targets according to a target positioning strategy. Most of these strategies are achieved by the means of fuzzy logic controllers and uniformly applied for every robot. The simulation experiments verified that the proposed method has a positive effectiveness for the navigation problem.

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Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm

Cited by 17 publications

References 10 publications

Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Real Time Neural Network Path Planning Algorithm for Robot

Fuzzy Logic Based Navigation for Multiple Mobile Robots in Indoor Environments

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